Beginning Adult Literacy Learners, Portfolios, and Self-Regulated Learning
Bibliographic record
Abstract
The use of self-regulated learning (SRL) strategies such as planning, goal-setting, monitoring, evaluating, and reflecting on ways to improve learning typically involve print-based literacy skills. Beginning adult English as a second language literacy learners (BELLs) who have had few prior experiences with formal, school-based learning are in the process of developing the formal SRL strategies associated with effective classroom learning. The use of portfolios in the classroom has been found to contribute to the development of both literacy and SRL strategies; however, investigations of BELLs’ portfolio use and SRL are scarce. To address this gap, 118 BELLs from 23 different classes were individually interviewed to investigate their experiences with and perceptions of portfolio use and assessment in their task-based language and literacy classes. Bi/multi-lingual interpreters conducted, transcribed, and translated the interviews into English. Students’ responses regarding the purpose of portfolios, the processes involved in using portfolios, their attitudes towards portfolios, and the influence of portfolios on their learning were thematically analyzed for evidence of SRL. Results revealed that (a) BELLs’ attitudes towards portfolios were generally positive in that portfolios helped them to organize their work for later review and allowed them to see their improvement, and (b) BELLs’ emergent understanding and use of portfolios as a tool for SRL was influenced by the high levels of teacher-regulation and the summative use of portfolio results for advancement in these classes. Findings are discussed in regard to the role of portfolios in developing SRL and their practical implications for literacy instruction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".